Overview of the ZINB-GRAN: Starting with the count matrix from scRNA-seq data as input, ZINB-GRAN first constructs a WGCN from gene expression data. (IMAGE)
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Overview of the ZINB-GRAN: Starting with the count matrix from scRNA-seq data as input, ZINB-GRAN first constructs a WGCN from gene expression data. Based on this WGCN, it builds an initial regulatory graph for the genes. The initial regulatory graph and gene expression profiles are then input into a GAE. The GAE model consists of a GCN and a scoring function: the GCNs serve as the encoder, learning the global regulatory structure and embedding it into gene representations, while the scoring function acts as the decoder, scoring the gene pairs’ representations and reconstructing the GRN. The GAE aligns the latent representation Z with the prior distribution. scRNA-seq: single-cell RNA sequencing; WGCN: weighted gene co-expression network; GAE: graph autoencoder; GCNs: graph convolutional networks; GRN: gene regulatory network.
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© Jianping Zhao, Junfeng Xia, Chunhou Zheng, et al. This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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